Import Upstream version 0.6.5~20141030

1 files changed, 136 insertions, 0 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/probabilistic/HellingerDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/probabilistic/HellingerDistanceFunction.java
new file mode 100644
index 00000000..4bd3de73
--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/probabilistic/HellingerDistanceFunction.java
@@ -0,0 +1,136 @@
+package de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2014
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.database.query.DistanceSimilarityQuery;
+import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceSimilarityQuery;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.similarityfunction.PrimitiveSimilarityFunction;
+import de.lmu.ifi.dbs.elki.math.MathUtil;
+import de.lmu.ifi.dbs.elki.utilities.Alias;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+
+/**
+ * Hellinger kernel / Hellinger distance are used with SIFT vectors, and also
+ * known as Bhattacharyya distance / coefficient.
+ * 
+ * This distance is appropriate for histograms, and is equal to A) L1
+ * normalizing the vectors and then B) using Euclidean distance. As this is
+ * usually much faster, this is the recommended way of handling such data.
+ * 
+ * Reference:
+ * <p>
+ * E. Hellinger<br />
+ * Neue Begründung der Theorie quadratischer Formen von unendlichvielen
+ * Veränderlichen<br />
+ * Journal für die reine und angewandte Mathematik
+ * </p>
+ * 
+ * TODO: support acceleration for sparse vectors
+ * 
+ * @author Erich Schubert
+ */
+@Reference(authors = "E. Hellinger", //
+title = "Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen", //
+booktitle = "Journal für die reine und angewandte Mathematik ", //
+url = "http://resolver.sub.uni-goettingen.de/purl?GDZPPN002166941")
+@Alias({ "hellinger", "bhattacharyya" })
+public class HellingerDistanceFunction extends AbstractNumberVectorDistanceFunction implements PrimitiveSimilarityFunction<NumberVector> {
+  /**
+   * Static instance.
+   */
+  public static final HellingerDistanceFunction STATIC = new HellingerDistanceFunction();
+
+  /**
+   * Hellinger kernel. Use static instance {@link #STATIC}!
+   */
+  @Deprecated
+  public HellingerDistanceFunction() {
+    super();
+  }
+
+  @Override
+  public double distance(final NumberVector fv1, final NumberVector fv2) {
+    final int dim1 = fv1.getDimensionality(), dim2 = fv2.getDimensionality();
+    final int mindim = (dim1 < dim2) ? dim1 : dim2;
+    double agg = 0.;
+    for(int d = 0; d < mindim; d++) {
+      final double v = Math.sqrt(fv1.doubleValue(d)) - Math.sqrt(fv2.doubleValue(d));
+      agg += v * v;
+    }
+    for(int d = mindim; d < dim1; d++) {
+      agg += Math.abs(fv1.doubleValue(d));
+    }
+    for(int d = mindim; d < dim2; d++) {
+      agg += Math.abs(fv2.doubleValue(d));
+    }
+    return MathUtil.SQRTHALF * Math.sqrt(agg);
+  }
+
+  @Override
+  public double similarity(final NumberVector o1, final NumberVector o2) {
+    // TODO: accelerate sparse!
+    final int dim1 = o1.getDimensionality(), dim2 = o2.getDimensionality();
+    final int mindim = (dim1 < dim2) ? dim1 : dim2;
+    double agg = 0.;
+    for(int d = 0; d < mindim; d++) {
+      agg += Math.sqrt(o1.doubleValue(d) * o2.doubleValue(d));
+    }
+    return agg;
+  }
+
+  @Override
+  public boolean isMetric() {
+    return true; // as this equals Euclidean in sqrt space
+  }
+
+  @Override
+  public <T extends NumberVector> DistanceSimilarityQuery<T> instantiate(Relation<T> database) {
+    return new PrimitiveDistanceSimilarityQuery<>(database, this, this);
+  }
+
+  @Override
+  public SimpleTypeInformation<? super NumberVector> getInputTypeRestriction() {
+    return TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    @Override
+    protected HellingerDistanceFunction makeInstance() {
+      return HellingerDistanceFunction.STATIC;
+    }
+  }
+}